This research examines Italy’s energy usage and its greenhouse gas emissions—CO2, CH4, and N2O—over the period 1960–2019, using a Bayesian network model on the data. It indicates there is a 70% likelihood CO2 will increase as the population increases, and a 66% likelihood that high fossil fuel emissions will persist. It also indicates high correlation among main drivers such as economic growth, population growth, urbanization, and overall energy use. These results allow us to see how socioeconomic transformations have impacted emissions over time. With the use of Bayesian networks, the research provides a solid methodology for projecting the impacts of climate change, one that could be crucial in assisting to make better choices in the development of policy. Lastly, this research provides significant, evidence-based information to policymakers to assist them to develop plans that lead us toward more sustainable energy systems—while maintaining the balance with economic development and not causing harm to the environment.